CHALLENGE

User Pain Points

Fashion users often face two core challenges: not knowing how to style their clothes and not knowing what to wear.

In daily life, they lack effective guidance and often feel overwhelmed by rapidly changing fashion trends.

Business Pain Points

Although the AI fashion market shows great potential, online adoption remains low and market share is fragmented. Despite a large user base, conversion is still limited, indicating significant untapped business opportunities.

How might we use AI to make outfit decisions effortless and enjoyable for users?

Key Features

Based on the identified user and business pain points, we defined three core features for the MVP stage:

01 - AI Outfit Advice

You can enter a sentence or upload a photo to generate your personalized outfit inspiration images.

02 - One Item, Multiple Styles

Get multiple outfit ideas from your uploaded item — expand your wardrobe without extra cost.

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03 - Virtual Try-On

Offer a virtual try-on experience for the generated outfits, helping you explore different styles at a low cost.

process

The design process followed the Double Diamond framework, iteratively refining our research and designs to address users’ needs.

Discover

Define

Develop

Deliver

Discover 🔍

Desk Research & Competitive Analysis:

Market Size

The North American women’s fashion market is substantial, while the integration of generative AI solutions is still at an early stage.

Competitor Landscape

Most existing solutions focus on wardrobe management or basic recommendations, offering limited personalization, low engagement, and fragmented user experiences.

Opportunity

AI-driven styling represents a high-potential blue ocean, with technical and personalization challenges, ideal for testing product-market fit.

Define 🎯

Persona

Information Architecture

Develop 🎨

User Flows

1 - New User Flow

Access app (not logged in) → Browse or try to generate → Log in to continue the experience

Designed a soft onboarding experience that encourages users to explore outfit inspirations first, then sign up or log in when engaging with core features.

2 - Core Flow A

Don‘t know what to wear?

Enter prompts or explore → Get oufit inspiration → Virtual try-on → Like / Share / Buy

3 - Core Flow B

Don‘t know how to style your clothes?

Upload a clothing item → Get oufit inspiration → Virtual try-on → Like / Share / Buy

4 - Additional Flow

Want more personalized recommendations?

Manage personal style profile & outfit history - Build personalized recommendations and track styling journey.

Deliver 📦

Designed 25+ MVP screens, created high-fidelity prototypes, and built a design system with 70+ reusable components.

Annotated design files for efficient handoff and collaborated closely with engineers to ensure smooth implementation.

Participated in weekly design reviews, working cross-functionally to deliver 0→1 design solutions and complete two full design iterations.

Design System

Responsive Design (APP/WEB)

Outcome

The MVP launch delivered encouraging results, validating both the product concept and design direction — even without large-scale promotion! ✨

The bounce rate of the MVP was around 20%, well below the industry’s healthy benchmark (~40%).

Users proactively shared positive feedback, praising the “refined and aesthetic design.”

The longest single-day usage exceeded 2 hours, indicating strong engagement and user interest.

POPWEAR

CHALLENGE

How might we use AI to make outfit decisions effortless and enjoyable for users?

Key Features

Business Goals

Validate Product Feasibility

Measure User Engagement